A Survey on Rendezvous Data Collection in Wireless Sensor Networks. Presented by Longfei Shangguan Supervisor:Dr.Yunhao Liu

Transcription

1 A Survey on Rendezvous Data Collection in Wireless Sensor Networks Presented by Longfei Shangguan Supervisor:Dr.Yunhao Liu

2 Roadmap Background Introduction of state-of-art solutions Future works References

3 Background Wireless Sensor Networks (WSNs) Numerous, spatially distributed sensor nodes Cooperatively monitor the target field Report sensory data back to the static sink

4 Background Environment Monitoring Ocean Surveillance Structure Monitoring Sustainable Network Design!!! Static sink + Multi-hop transmissions Unevenly distributed traffic Energy bucket effect (a.k.a. the energy hole [1]) Limitations of the Static Sink - #1 Long-term deployment requirement Limited energy supply [1] Stephan Olariu, and Ivan Stojmenovic, Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting, in Proc. of IEEE INFOCOM, 2006

5 Background A Promising Solution Mobile sink (mobile user) Advantages Balanced traffic load among sensor nodes Instant communication between user and network A variety of new applications Forest fire detection Temporary control in vineyard Parking site search

6 Background Static vs. Mobile Collection Delay Energy Consumption Network Lifespan Stationary Data Collection Low High short Rendezvous Data Collection High low long

7 Background Direct-contact Data Collection Stochastic TSP Label-covering Rendezvous-based Data Collection Fixed track Tree-based Clustering-based

8 Background Several nodes serve as rendezvous points (RPs) Others nodes send data to the closest RP Mobile sink visits RPs to collect sensory data mobile sink source node rendezvous point

9 Background sink sink Stationary Data Collection Sensor Node Hot Spot Point Rendezvous Data Collection Rendezvous Point

10 Fixed Track Approach(1) Straight-line actuator path Single actuator in the network A. Kansal, A. A. Somasundara, D. D. Jea, M. B. Srivastava, and D. Estrin. Intelligent Fluid Infrastructure for Embed-ded Networks. In Proceeding of MobiSys, pp , 2004.

11 Fixed Track Approach(2) Straight-line actuator path Multiple actuators in the network D. Jea, A. Somasundara, and M. Srivastava. Multiple Con-trolled Mobile Elements (Data Mules) for Data Collection in Sensor Networks. In Proceedings of IEEE DCOSS, vol of LNCS, pp , 2005.

12 Clustering-based Approach No location information about Sensors Single actuator in the network Techniques: K-hop Dominate Set Minimum Hop Tree Overlay Graph Distributed TSP algorithm J. Rao and S. Biswas. Joint Routing and Navigation Proto-cols for Data Harvesting in Sensor Networks. In Proceedings of IEEE MASS, pp , 2008.

13 Tree-based Approach(1) A fixed actuator path with arbitrary shape Length-constrained Single actuator in the network G. Xing, T. Wang, W. Jia, and M. Li. Rendezvous De-sign Algorithms for Wireless Sensor Networks with a Mobile Base Station. In Proceeding of ACM MobiHoc, pp , 2008.

14 Problems RP 2 RP 1 Network lifespan is stilled constrained, due to the uneven traffic load. RP 3 RP 4 Sensor Node Hot Spot Point Rendezvous Point Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia,, Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, In Proceedings of the 28th IEEE Real-Time Systems Symposium (RTSS), 2007

15 Problems RP 1 RP 3 sufficiently long lifetime of the network is achieved. RP 2 Sensor Node Rendezvous Point

16 Tree-based Approach(2) The Rendezvous Data Collection Problem Given sources { s i }, routing trees T i ( V i, E i ), determine: 1) A set of RPs R = {r i } and their sequence forming a trajectory U of the mobile sink that is no longer than L = v MS D; 2) A set of workload balanced routing trees covering all source nodes in S; Hardness: General case is NP-Hard. Luo Mai, Longfei Shangguan, Junzhao Du, Zhenjiang Li, and Mo Li Load Balanced Rendezvous Data Collection in Mobile Wireless Sensor Networks. In Proceeding of IEEE MASS, 2011.

17 Tree-based Approach(2) Basic Idea: Reference Node & Reference Structure; Sensor Node Median Node Rendezvous Point

18 Tree-based Approach(2) Median Searching Algorithm: r The distance sum of the global sensory data transmission links D Tr (r) is determined. Each D Tv (v) is therefore determined.

19 Tree-based Approach(2) RPS-LB Algorithm: median Sensor Node Median node Rendezvous Point

20 Tree-based Approach(2) Theoretical Analysis: Theorem 1: 2: 3: Among TSP(R) < all L, possible always holds sub-trees before of size RPS-LB s Y, the termination, total number distances of from nodes where all Rin i source is the the largest RPs nodes set branch in found the induced routing the i-time tree by the to iteration. median the median sub-tree sub-tree is always is always minimized. minimized.

21 Tree-based Approach(2)

22 Future Works Evolving Data Collection Structure RP 1 RP 3 RP 1 RP 3 RP 2 RP 2

23 References A. Kansal, A. A. Somasundara, D. D. Jea, M. B. Srivastava, and D. Estrin. Intelligent Fluid Infrastructure for Embed-ded Networks. In Proceeding of MobiSys, pp , 2004 D. Jea, A. Somasundara, and M. Srivastava. Multiple Con-trolled Mobile Elements (Data Mules) for Data Collection in Sensor Networks. In Proceedings of IEEE DCOSS, vol of LNCS, pp , J. Rao and S. Biswas. Joint Routing and Navigation Proto-cols for Data Harvesting in Sensor Networks. In Proceedings of IEEE MASS, pp , G. Xing, T. Wang, W. Jia, and M. Li. Rendezvous De-sign Algorithms for Wireless Sensor Networks with a Mobile Base Station. In Proceeding of ACM MobiHoc, pp , Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia,, Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, In Proceedings of RTSS, 2007 Luo Mai, Longfei Shangguan, Junzhao Du, Zhenjiang Li, and Mo Li Load Balanced Rendezvous Data Collection in Mobile Wireless Sensor Networks. In Proceeding of IEEE MASS, 2011.

24 Homepage: long.srfid.org Thank you!